Next week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):
Unlocking Brain-Inspired Computer Vision: a Multi-Disciplinary, High-Throughput Approach
by Nicolas Pinto Department of Brain and Cognitive Sciences Massachusetts Institute of Technology
Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: Friday, November 20, 14h30
The construction of artificial vision systems and the study of biological vision are naturally intertwined as they represent simultaneous efforts to forward and reverse engineer systems with similar goals. While exploration of the neuronal substrates of visual processing provides clues and inspiration for artificial systems, artificial systems can in turn serve as important generators of new ideas and working hypotheses. However, while systems neuroscience has so far provided inspiration for some of the "broad-stroke" properties of the visual system (e.g. hierarchical organization, synaptic integration of inputs and threshold, normalization, plasticity, etc), much is still unknown. Even for those qualitative properties that most biological-inspired models hold in common, experimental data currently provide little constraint on their key parameters. Consequently, it is difficult to truly evaluate a set of computational ideas, since the performance of any one model depends strongly on its particular instantiation - e.g. the size of the pooling kernels, the number of units per layer, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is very large, and the typical computational cost of evaluating one particular model is high, the space of possible model instantiations usually goes largely unexplored. Compounding the problem, even if a set of computational ideas are on the right track, the instantiated "scale" of those ideas is typically small (e.g. in terms of dimensionality and amount of learning experience provided). Thus, when a model fails to approach the abilities of the visual system, we are left uncertain whether this failure is because we are missing a fundamental idea, or because the correct "parts" have not been tuned correctly, assembled at sufficient scale, or provided with sufficient natural experience.
To pave a possible way forward, we have begun developing a high-throughput approach to expansively explore a large range of biologically-inspired models - including models of larger, more realistic scale - leveraging recent advances in commodity stream processing hardware (high-end GPUs and Playstation 3’s Cell processors) and scientific cloud computing (e.g. Amazon EC2). In analogy to high-throughput screening approaches in molecular biology and genetics, we generated and trained thousands of potential network architectures and parameter instantiations, and "screened" the visual representations produced by these models using an object recognition task. From these candidate models, the most promising were selected for further analysis. We have shown that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature, and that it can offer insight into which computational ideas are most important for achieving this performance.
As the scale of available computational power continues to expand, we believe that this approach holds great potential both for accelerating progress in artificial vision, and for generating new, experimentally-testable hypotheses for the study of biological vision.